Sequential delivery of advertising content across media devices

ABSTRACT

The technology relates to sequential or tailored delivery of advertising content across a plurality of media conduits. The invention achieves sequential story telling for advertising campaigns in place of single-series advertising, by delivering over a period of several sessions on a variety of devices. An advertiser can air a campaign on a consumer individual&#39;s cell phone device, continue the second portion of the campaign via a desktop browser session, and conclude with the third portion of the campaign on the individual&#39;s OTT device. The technology provides advanced controls over targeting and scheduling with high precision.

CLAIM OF PRIORITY

This application is a continuation of application Ser. No. 15/219,264,filed Jul. 25, 2016, now U.S. Pat. No. 9,980,011, which claims thebenefit of priority under 35 U.S.C. § 119(e) to U.S. provisionalapplication Ser. No. 62/264,764, filed Dec. 8, 2015, and to U.S.provisional application Ser. No. 62/196,618, filed Jul. 24, 2015, all ofwhich are incorporated herein by reference in their entireties.

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.15/219,259, filed Jul. 25, 2016, entitled “TARGETING TV ADVERTISINGSLOTS BASED ON CONSUMER ONLINE BEHAVIOR”, Ser. No. 15/219,262, filedJul. 25, 2016, entitled “CROSS-SCREEN OPTIMIZATION OF ADVERTISINGPLACEMENT”, Ser. No. 15/219,268, filed Jul. 25, 2016, entitled“CROSS-SCREEN MEASUREMENT ACCURACY IN ADVERTISING PERFORMANCE”, and toprovisional application Ser. No. 62/196,637, filed Jul. 24, 2015, Ser.No. 62/196,898, filed Jul. 24, 2015, Ser. No. 62/196,592, filed Jul. 24,2015, Ser. No. 62/196,560, filed Jul. 24, 2015, Ser. No. 62/264,764,filed Dec. 8, 2015, Ser. No. 62/278,888, filed Jan. 14, 2016, Ser. No.62/290,387, filed Feb. 2, 2016, and Ser. No. 62/317,440, filed Apr. 2,2016, all of which are incorporated herein by reference in theirentireties.

TECHNICAL FIELD

The technology described herein generally relates to innovativemarketing and advertising techniques, and more particularly relates tofine-tuned delivery of content programming from advertisers and brandmanagers to consumers, in a controlled sequence and/or with differentversions of the content directed to different categories of consumer,across multiple categories of device.

BACKGROUND

Video advertisements are among the most advanced, complicated, andexpensive, forms of advertising content. Beyond the costs to producevideo content itself, the expense of delivering video content over thebroadcast and cable networks remains considerable, in part becausetelevision (TV) slots are premium advertising space in today's economy.Adding online consumption to the list of options available to any givenconsumer, only leads to greater complexity to the process ofcoordinating delivery of video adverts to a relevant segment of thepublic. This complexity means that the task of optimizing delivery ofadvertising content today far exceeds what has traditionally beennecessary, and what has previously been within the capability ofexperienced persons. In short, today's complexities require specificallytailored technological solutions, and take the decision making out ofthe hands of skilled people by utilizing computer methods that are ableto handle a huge number of factors, and at a speed, that humans couldnot possibly cope with.

Even though the average length of a video advertisement is only about 30seconds, it remains critical to the advertiser's message to ensure thata consumer is attentive to the full content. Tailoring that content in away that keeps it interesting and relevant to a given consumer cantherefore be key to the success of an advertising campaign.

Consequently, video advertising has advanced beyond repeated broadcastsof the same basic take. Among the techniques deployed by advertiserstoday are: telling short commercial stories via video advertising in anattempt to capture consumers' attention; delivering different versionsof the same basic video across different media, and at different times,in the hope of tailoring content to different cross sections of thepopulation; and creating and delivering several abbreviated versions ofa basic full-length video in order to reinforce a particular theme orpunchline in consumers' minds.

Consequently, there are many important considerations that influence anadvertiser's selection of advertising inventories and the type ofcontent to deliver. The considerations include factors such as: time ofday the advertisement will play, desired number of impressions, type ofaudience the advertiser wishes to reach, and the price of theadvertising time slot.

Nevertheless, advertisers are heavily dependent on information theyreceive from media conduits for assistance in deciding where and whencontent should be delivered, as well as assessing effectiveness of thatdelivery when making decisions on subsequent strategies. The decisionsof how to deliver content, and what form that content takes, areparticularly influenced by information about the viewing public madeavailable by the content providers. For example, content providers caninform advertisers which demographics are likely viewers of a givenprogram, according to time of day and program content. However, today'srich media environment demands attention to more factors when decidingwhen to deliver advertising content and to which types of device.

Furthermore, in the context of today's advertising, it is both importantbut difficult to be nimble and flexible in content delivery: anadvertiser wants to be able to react quickly to changes in marketconditions and to an appreciation that an initial strategy is notoptimal, as well as to capitalize on the consumer's access to manydifferent viewing platforms.

Many advertisers, due to various constraints such as cost and lack ofinformation, will play the same commercial repeatedly in hopes ofreaching the widest possible demographic and in the simplest manner.Such an approach, though simple, is highly inefficient, as the sameconsumer may see the same advert dozens or more times, yet a huge numberof potentially valuable consumers may not see the advert at all, eitherbecause they predominantly view content on a different type of device orbecause they do not typically watch at the time the advert is regularlyscheduled to run.

Additionally, media conduits are effectively siloed and produce anenvironment in which it is not possible to separate a campaign into manylinear parts of a whole story. For example, Internet companies Googleand Facebook are considered as media conduits because they have theirown platforms for broadcasting content to a dedicated population ofconsumers. Each such company limits exchange of data to within their ownproperties; thus it is not possible to coordinate an advertisingcampaign across both platforms at the same time. Similarly, anadvertiser cannot easily coordinate delivery of content between, sayFacebook, and a TV content provider such as DirecTV. Consequently, manyadvertising agencies divide their campaign budgets between TV and onlinedelivery.

Furthermore, it has not been possible with today's tools to trackexactly which person has watched a particular advertisement because itis not possible to aggregate information from all the available mediaconduits on which that individual might have viewed content. Whileinformational tools today are able to quantify viewer participation bycalculating views per media device or provider, and infer, based onavailable census data, which types of individuals are likely to view anadvertisement, the ability to aggregate exact viewer behavior acrossmultiple media conduits has not been possible to do with useful accuracyor speed. As such, advertisers anticipate that in order to reach thedesired audience, they will need to repeatedly play the same short clipeither across many media conduits or target a selection of popular mediaconduits for multiple successive broadcasts of the same content ornon-redundant versions of it. But the challenge of anticipating whichviewers will actually view the content remains.

Therefore, today, attempting to tell a multi-chapter story over multipleadvertising slots bears a high risk that the audience will miss one ormore key episodes of the story and thereby miss out on the message, orfind it to be confusing and fragmented. This means that “direct responseadvertising”, and advertising on digital, mobile, and TV platforms areeach planned separately, even though the practical nature of messagesequencing remains the same, regardless of platform.

Assessing whether a user has viewed TV delivered content hashistorically been challenging because it is difficult to establishwhether a person actually watched the show or segment as it was beingbroadcast. Ratings companies, such as Nielsen, use a panel approach,which by definition involves polling a fixed group of consumers thathave been selected by the ratings companies to be representative of thepopulation at large.

The advent of “Smart TV's” such as those manufactured by Samsung, LG,and Vizio has, however, provided more reliable means of measuring thisdata. Data from Smart-TV's can be used to produce measurements that areat least equivalently informative to those relied on by Nielsen, andoffer the prospect of being superior for a number of reasons: the datathat can be received from a Smart-TV is richer than a simple yes/noresponse to whether a given viewer watched a particular program; thereare many more SmartTV's in circulation than even the largest panelsdeployed by ratings companies, and that number continues to increaseover time; and Smart-TV data can potentially be linked to other dataabout a given consumer. This means that it no longer makes sense to relyon an old-fashioned technique that relies on a panel of consumers tovalidate a model.

Nevertheless, online media distributors such as Google don't have datafrom SmartTV's. Given this, the state of the art in advertisingstrategies differs across different media. For example, digitaladvertising is able to target based on known online behaviors, whereasTV advertising strategy is based on census data and is focused onreaching particular demographics.

The discussion of the background herein is included to explain thecontext of the technology. This is not to be taken as an admission thatany of the material referred to was published, known, or part of thecommon general knowledge as at the priority date of any of the claimsfound appended hereto.

Throughout the description and claims of the application the word“comprise” and variations thereof, such as “comprising” and “comprises”,is not intended to exclude other additives, components, integers orsteps.

SUMMARY

The instant disclosure addresses the processing of Consumer andadvertising inventory in connection with optimizing placement ofsequences of advertising content across display devices. In particular,the disclosure comprises methods, for doing the same, carried out by acomputer or network of computers. The disclosure further comprises acomputing apparatus for performing the methods, and computer readablemedia having instructions for the same. The apparatus and process of thepresent disclosure are particularly applicable to Video content inonline and TV media.

A method for delivering advertising content sequentially to a consumeracross two or more display devices, the method comprising: receiving apricepoint and one or more campaign descriptions from an advertiser,wherein each of the campaign descriptions comprises a schedule forsequential delivery of two or more items of advertising content acrosstwo or more devices accessed by a consumer, wherein the devices includea TV and one or more mobile devices, and a target audience, wherein thetarget audience is defined by one or more demographic factors; defininga pool of consumers based on a graph of consumer properties, wherein thegraph contains information about the two or more TV and mobile devicesused by each consumer, demographic and online behavioral data on eachconsumer and similarities between pairs of consumers, and wherein thepool of consumers comprises consumers having at least a thresholdsimilarity to a member of the target audience; receiving a list ofinventory from one or more content providers, wherein the list ofinventory comprises one or more slots for TV and online; identifying oneor more advertising targets, wherein each of the one or more advertisingtargets comprises a sequence of slots consistent with one or more of thecampaign descriptions, and an overall cost consistent with thepricepoint; allocating the advertising content of the one or morecampaign descriptions to the one or more advertising targets; for eachslot in the sequence of slots, making a bid on the slot consistent withthe pricepoint; for a first slot where a bid is a winning bid:instructing a first content provider to deliver a first item ofadvertising content in the first slot and a first performance tag to thepool of consumers on a first device; receiving a first datum from thefirst performance tag to validate whether a particular consumer viewedthe first item of advertising content on the first device; and dependingon the first datum, for a second slot where a bid is a winning bid,instructing a second content provider to deliver a second item ofadvertising content in the second slot and a second performance tag tothe particular consumer on a second device, wherein at least one of thefirst device and the second device is a TV.

The technology still further includes a method for applying a machinelearning technique to the first and second performance tags, in order toimprove the allocating the advertising content of the one or morecampaign descriptions to the one or more advertising targets.

The technology also comprises a method for delivering advertisingcontent sequentially to a consumer across two or more display devices,the method comprising: identifying a consumer based on one or moredemographic factors, and based on one or more indices of similarity tomembers of a target audience; identifying two or more display devicesaccessible to the consumer, wherein the two or more display devicescomprise at least one TV and at least one mobile device; purchasing twoor more slots of advertising inventory wherein one or more slots aredelivered on a TV, and one or more slots are delivered on a mobiledevice; instructing a first media conduit to deliver a first item ofadvertising content to the consumer on a first device, and, if the mediaconduit confirms that the consumer was exposed to the first item ofadvertising content, instructing a second media conduit to deliver asecond item of advertising content to the consumer on a second device,wherein at least one of the first and second devices is a TV, and atleast one of the first and second devices is a mobile device.

The technology also comprises a method of controlling sequentialdelivery of cross-screen advertising content to a consumer, the methodcomprising: determining that the consumer is a member of a targetaudience; identifying a first and second device accessible to theconsumer; receiving instructions for placement of a first and seconditem of advertising content on the first and second device, consistentwith an advertising budget and the target audience; causing a firstmedia conduit to deliver the first item of advertising content to thefirst device; and when the first item of advertising content has beenviewed by the consumer, causing a second media conduit to deliver thesecond item of advertising content to the second device, wherein thefirst and second device comprise a TV and a mobile device.

The present disclosure further provides for computer readable media,encoded with instructions for carrying out methods described herein andfor processing by one or more suitably configured computer processors.

The present disclosure additionally includes a computing apparatusconfigured to execute instructions, such as stored on a computerreadable medium, for carrying out methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows, diagrammatically, relationships between parties thatcontribute to delivery of advertising content, such as advertisers, anadvertising exchange, media conduits, and consumers.

FIG. 2 shows an exemplary graph that represents consumers, the graphhaving several nodes, various pairs of which are connected by edges.

FIG. 3 shows a representative node of a consumer graph, and itsattributes.

FIGS. 4A and 4B show a process for generating a consumer graph.

FIG. 5 shows a schematic of a population sample.

FIGS. 6A, 6B shows a process for delivering sequential advertisingcontent.

FIG. 7 shows an exemplary computing apparatus for performing a processas described herein.

FIGS. 8-1 and 8-2 show, split over two panels, an exemplary computerinterface for use by an advertiser in selecting inventory according tomethods described herein.

DETAILED DESCRIPTION

The instant technology is directed to computer-implemented methods forimproving advertising reach, brand lift, and performance. Specifically,the invention relates to control of advertisements targeted to narrowlytailored market segments, and methods of achieving sequential targetingacross devices and across a plurality of media conduits.

The system facilitates protracted messaging over several sessions on avariety of devices. In this way, a consumer may begin to watch a firstportion of an advertising campaign on their mobile device, such as acell phone, may continue to view the second portion of the campaign viaa desktop browser session, and concludes with a third portion on theirOTT device. This permits the advertiser to tell an elaborate storybecause they are not limited to a single session or multiple disjointsessions. The system can thereby create a seamless story tellingexperience for audiences.

The system permits flexibility in targeting of market segments, in arobust manner. For example, advertisers are able to either design theircampaign using sequential targeting across devices that then playmultiple versions of an advertisement, or, they can choose to retargetwith a single version for a specified frequency. The invention providesadvanced controls over targeting and scheduling with high precision.

The technology herein will be useful to a purchaser of advertisinginventory who requires a high degree of control, and desires asophisticated approach to population targeting. Such users willtypically be large purchasers of advertising inventory, as well asmanagers of brand campaigns, media campaign consultancies, andadvertising agencies, among others.

Thus, if an advertiser wants to control the number of times a user isexposed to an advertisement, the technology is not limited to just onedevice for that ability to control. Such control can include specifyinghow many times to serve an advertisement to a particular person, as wellas applying additional constraints such as imposing an upper limit ofthe number of total deliveries of the advertisement per device. It issimilarly possible to control the frequency of delivery of anadvertisement, as well as the total number of deliveries, by introducingglobal (across all devices) and local (specific to a particular device)frequency caps.

Additionally, it is possible to create a sequential media plan: if it isknown which consumers saw a particular advertisement on a particulardevice, it would be desirable to target them on a different device, suchas with a competing advertisement, or with a subsequent version of thefirst advertisement that relies on an already established level offamiliarity by that consumer. For example, the advertiser may want toshow a particular advertisement first to a consumer on TV, and thenfollow that up with a targeted advertisement that is more pertinent tothe user, on their mobile phone, and then still further with otheradvertising content on the consumer's desktop computer or laptop.Scheduling such as this becomes possible, due to the implementation ofuser mapping across various devices: it is thus possible to know thatthe same user is being targeted.

The instant technology includes implementation of a device graph, whichutilizes data from OEM's, etc., to aggregate information for specificusers across multiple different devices. The device graph addresses theproblem that individual media conduits typically won't do matching ofconsumer information with other providers. A demand side platform thattakes in data from different conduits, for example Facebook and Googledata are input as different “devices”, can match the various consumerinformation by IP address, behavior etc., using the graph technologyherein.

Advertising Functions

Relationships between entities in the business of purchase, delivery andconsumption of advertising content are depicted in FIG. 1. As can beseen, the advertising ecosystem is complex, involves many differententities, and many different relationships.

An advertiser 101 is a purchaser of advertising inventory 109. Anadvertiser may be a corporation that exercises direct control over itsadvertising functions, or it may be an agency that manages advertisingrequirements of one or more clients, usually corporate entities. Theadvertiser intends to make advertising content 103 (also an“advertisement” herein) available to one or more, typically a populationof, consumers 105, on one or more devices 107 per consumer.

Devices 107 include, for a given consumer, one or more of: TV's(including SmartTV's), mobile devices (cell phones, smartphones, mediaplayers, tablets, notebook computers, laptop computers, and wearables),desktop computers, networked photo frames, set top boxes, gamingconsoles, streaming devices, and devices considered to function withinthe “Internet of Things” such as domestic appliances (fridges, etc.),and other networked in-home monitoring devices such as thermostats andalarm systems.

The advertising content 103 has typically been created by the advertiser101 or a third party with whom the advertiser has contracted, andnormally includes video, audio, and/or still images that seek to promotesales or consumer awareness of a particular product or service.Advertising content 103 is typically delivered to consumers via one ormore intermediary parties, as further described herein.

Advertising content is typically of two different types: branding, anddirect-response marketing. The timeframe is different for these twotypes. Branding promotes awareness; direct response marketing isdesigned to generate an immediate response. For example, an automobilemanufacturer may put out direct response marketing material into themarket place, and wants to measure responses by who went to a dealershipor website after seeing an advertisement. The methods herein can beapplied to both types of advertising content, but the measurement ofeffectiveness is different for the two types: for example, effectivenessof branding is measured by GRP's (further described elsewhere herein),and results of direct response marketing can be measured by, forexample, website visits.

When delivered to a mobile device such as a phone or a tablet,advertising content 103 may additionally or alternatively take the formof a text/SMS message, an e-mail, or a notification such as an alert, abanner, or a badge. When delivered to a desktop computer or a laptopcomputer or a tablet, the advertising content 103 may display as apop-up within an app or a browser window, or may be a video designed tobe played while other requested video content is downloading orbuffering.

Consumers 105 are viewers and potential viewers of the advertisingcontent 103 and may have previously purchased the product or servicethat is being advertised, and may—advantageously to the advertiser—belearning of the product or service for the first time when they view theadvertising content 103.

Advertising inventory 109 (also inventory or available inventory,herein) comprises available slots, or time slots 117, for advertisingacross the several media interfaces, or conduits 111, through whichconsumers access information and advertising content. Such mediainterfaces include TV, radio, social media (for example, onlinenetworks, such as LinkedIN, Twitter, Facebook), digital bill boards,mobile apps, and the like. Media conduits 111 may generate their owncontent 113, or may be broadcasting content from one or more othercontent providers or publishers 115. For example, a cable company is amedia conduit that delivers content from numerous TV channel producersand publishers of content. Media interfaces may also be referred to ascontent providers, generally, because they deliver media content 113 (TVprograms, movies, etc.) to consumers 105. One aspect of the technologyherein includes the ability to aggregate inventory 109 from more thanone type of media interface or content provider. Media conduits 111 alsodeliver advertising content 103 that has been purchased for delivery attime slots 117, to consumers 105 for viewing on various devices 107. Apublisher 115 is typically a content owner (e.g., BBC, ESPN).

A slot 117 is a time, typically expressed as a window of time (1 minute,2 minutes, etc.) at a particular time of day (noon, 4:30 pm, etc., or awindow such as 2-4 pm, or 9 pm-12 am), or during a specified broadcastsuch as a TV program, on a particular broadcast channel (such as a TVstation, or a social media feed). An available slot is a slot in theinventory that an advertiser may purchase for the purpose of deliveringadvertising content. Typically it is available because anotheradvertiser has not yet purchased it. As further described herein, a slotmay additionally be defined by certain constraints such as whether aparticular type of advertising content 103 can be delivered in aparticular slot. For example, a sports equipment manufacturer may havepurchased a particular slot, defined by a particular time of day on aparticular channel, and may have also purchased the right to excludeother sports equipment manufacturers from purchasing slots on the samechannel within a certain boundary—in time—of the first manufacturer'sslot. In this context, a “hard constraint” is a legal or otherwisemandatory limitation on placing advertising in particular time slots orwithin specified media. A “soft constraint” refers to desired(non-mandatory) limitations on placing advertising in particular timeslots within specified media. “Constraint satisfaction” refers to theprocess of finding a solution to a set of constraints that imposeconditions that the variables must satisfy. The solution therefore is aset of values for the variables that satisfies all constraints.

Information is intended to mean, broadly, any content that a consumercan view, read, listen to, or any combination of the same, and which ismade available on a screen such as a TV screen, computer screen, ordisplay of a mobile device such as a tablet, smart-phone, orlaptop/notebook computer, a wearable such as a smart-watch, fitnessmonitor, or an in-car or in-plane display screen. Information isprovided by a media interface 111 such as a TV or radio station, amulti-channel video programming distributor (MVPD, such as a cable TVprovider, e.g., Comcast), or an online network such as Yahoo! orFacebook.

The communication between the advertisers and the media conduits can bemanaged by up to several entities, including: a demand-side platform(DSP) 123, an advertising exchange 119, and a supply-side provider 121.In FIG. 1, advertisers or agencies 101 provide advertising content 103to a DSP, and that advertising content is ultimately provided to a mediaconduit for delivery to consumers, once the advertiser has beensuccessful in purchasing a slot of inventory. An advertising exchange119 (also, exchange herein) is an environment in which advertisers canbid on available media inventory. The inventory may be digital such asvia online delivery over the Internet, or via digital radio such asSiriusXM, or may be analog, such as via a TV channel such as ESPN, CNN,Fox, or BBC, or an FM/AM radio broadcast. An advertising exchange 119typically specializes in certain kinds of content. For example, SpotXspecializes in digital content, WideOrbit specializes in programmaticTV.

Supply-side provider (SSP) 121 is an intermediary that takes inventory109 from one or more media conduits 111, and makes it available to ademand-side provider (DSP) 123, optionally via exchange 119, so thatadvertisers can purchase or bid on the inventory when deciding how toposition advertising content 103. SSP's have sometimes been categorizedas public or private depending on whether a media conduit is able tolimit the identity and number of advertisers who have access to theinventory. In some situations, an SSP interacts directly with a DSPwithout the need for an advertising exchange 119; this is true if thefunctions of an advertising exchange that a purchaser of advertisingcontent relies on are performed by one or both of the DSP and SSP. Thetechnology herein is particularly suited for being implemented and beingcarried out by a suitably-configured DSP.

In one configuration, an advertising exchange 119 interfaces between asupply side provider (SSP) 121 and a demand side provider (DSP) 123. Theinterfacing role comprises receiving inventory 109 from one or moreSSP's 121 and making it available to the DSP, then receiving bids 125 onthat inventory from the DSP and providing those bids 125 to the SSP.Thus, a DSP makes it possible for an advertiser to bid on inventoryprovided by a particular SSP such as SPotX, or WideOrbit. In someconfigurations, the DSP takes on most or all of the role of anadvertising exchange.

In one embodiment of the technology herein, a DSP provides a schedulefor an advertising campaign, which, if approved by the advertiser, theDSP has to purchase on its behalf and arrange for the execution of thecampaign. The SSP controls delivery of the advertising content to themedia conduits.

An advertising campaign (or campaign) is a plan, by an advertiser, todeliver advertising content to a particular population of consumers. Acampaign will typically include a selection of advertising content (suchas a particular advertisement or various forms of an advertisement, or asequence of related advertisements intended to be viewed in a particularorder), as well as a period of time for which the campaign is to run(such as 1 week, 1 month, 3 months). An advertiser typically transmits acampaign description 127 to an advertising exchange 119 or a DSP 121,and in return receives a list of the inventory 109 available. A campaigndescription 127 may comprise a single item of advertising content 103and one or more categories of device 107 to target, or may comprise aschedule for sequential delivery of two or more items of advertisingcontent 103 across one or more devices 107. A campaign description 127may also comprise a description of a target audience, wherein the targetaudience is defined by one or more demographic factors selected from,but not limited to: age range, gender, income, and location.

The DSP 123 then provides an interface by which the advertiser 101 canalign its campaign descriptions 127 against inventory 109 and purchase,or bid on, various slots 117 in the inventory. The DSP 123, or anexchange 119, may be able to provide more than one set of inventory thatmatches a given campaign description 127: each set of inventory thatmatches a given campaign description is referred to herein as anadvertising target 129. The advertiser 101 may select from among a listof advertising targets, the target or targets that it wishes topurchase. Once it has purchased a particular target, the SSP 121 isnotified and delivery instructions 137 are sent to the various mediaconduits 111 so that the advertising content 103 can be delivered in theapplicable time slots 117, or during selected content 113, to therelevant consumers.

A purchase of a given slot is not simply a straightforward sale at agiven price, but is achieved via a bidding process. The DSP will placebids on a number of slots, and for each one, will have identified a bidprice that is submitted to the SSP. For a winning bid, the SSP deliversthe advertising content to the media conduit, and ultimately theconsumer. Bids are generally higher for specific targeting than forblanket targeting.

The bidding process depends in part on the type of advertising content.TV content can be scheduled in advance, whereas for online content, thetypical bid structure is just-in-time' bidding: the advert is deliveredonly if a particular consumer is seen online. In general, the methodsherein are independent of bidding process, and are applicable to any ofthe bidding methods typically deployed, including real-time-bidding, aswell as bidding that exploits details of programmatic TV data, asdescribed in U.S. patent application Ser. No. 15/219,259, entitled“TARGETING TV ADVERTISING SLOTS BASED ON CONSUMER ONLINE BEHAVIOR”, andfiled on Jul. 24, 2016. Where real-time bidding (RTB) it may be basedon, e.g., protocols such as RTB 2.0-2.4, see Internet Advertisers Bureauat www.iab.com/guidelines/real-time-bidding-rtb-project/).

By serving a tag with a given online ad, by using a protocol such asVPAID (en.wikipedia.org/wiki/Mixpo) or VAST (video advertisement servingtemplate), the tag collects data including whether a consumer clickedon, or viewed, the content. The tag typically contains a number of itemsof data relating to how a consumer interacted with the advertisingcontent. The items of data can be returned to the SSP and/or the DSP inotder to provide feedback on the circumstances of delivery of theadvertisement. For example, the items of data can include a datumrelating to whether a user clicked on a video online. Certain items ofdata correspond to events that are referred to in the industry as“beacon” events because of their salience to an advertiser: for examplea beacon event can include the fact that a user stopped a video segmentbefore it completed.

The process of generating advertising targets may also depend one ormore campaign requirements. A campaign requirement, as used herein,refers to financial constraints such as a budget, and performancespecifications such as a number of consumers to target, set by anadvertiser or other purchaser of advertising inventory. Campaignrequirement information is used along with campaign descriptions whenpurchasing or bidding on inventory.

DSP's 123 also provide advertisers 101 with data on consumers anddevices, aggregated from various sources. This data helps an advertiserchoose from the inventory, those time slots and media conduits that willbest suit its goals.

Data used by DSP's may include census data 131, or data on specificconsumers and devices 133. Census data 131 includes data on a populationthat can be used to optimize purchase of inventory. Census data 131 cantherefore include demographic data such as age distribution, incomevariations, and marital status, among a population in a particularviewing region independent of what media interfaces the members of thepopulation actually view. Census data 131 can be aggregated from avariety of sources, such as state and county records, and U.S. CensusBureau data.

A data management platform (DMP) 135 can provide other types of thirdparty data 133 regarding consumers and the devices they use to the DSP.Examples of DMP's include: Krux (www.krux.com), Exelate (Internet atexelate.com), Nielsen (www.nielsen.com/us/en/about-us.html), Lotame(www.lotame.com). The consumer and device data 133 that is delivered toa DSP from a third party provider may complement other consumer anddevice data 143 that is provided by the media conduits. Data onconsumers and the devices they use that is relevant to an advertiserincludes matters of viewing habits as well as specific behavioral datathat can be retrieved directly from a media conduit. For example, asfurther discussed elsewhere herein, when a media conduit serves anadvertisement to a consumer, the conduit can collect information on thatuser's manner of access to the advert. Due to the volume of datainvolved, after a relatively short period of time, such as 14 days, amedia conduit may not be able to furnish any information on a particularconsumer. In that instance, the DSP can get data on that user from athird party such as a DMP. Third parties can get data offline as well.As used herein, an offline event is one that happens independently ofthe Internet or a TV view: for example, it can include purchase of anitem from a store and other types of location-based events that anadvertiser can view as significant. Data can be shared between theentities herein (e.g., between a DMP and a DSP, and between DSP and SSP,and between media conduits and a SSP or advertising exchange) using anycommonly accepted file formats for sharing and transfer of data: theseformats include, but are not limited to: JSON, CSV, and Thrift, as wellas any manner of text file appropriately formatted.

An impression refers to any instance in which an advertisement reaches aconsumer. On a TV, it is assumed that if the TV is broadcasting theadvertisement then an individual known to be the owner of, or a regularviewer of, that TV will have been exposed to the advertisement, and thatdisplay counts as an impression. If multiple persons are in the samehousehold then the number of impressions may equal the number of personswho can view that TV. In the online environment, an impression occurs ifa consumer is viewing, say, a web-page and the advertisement isdisplayed on that web-page such as in the form of a pop-up, or if theuser has clicked on a link which causes the advertisement to run.

An audience segment is a list of consumers, de-identified from theirpersonally identifiable information using cookie syncing or othermethods, where the consumers belong to a type (income, gender,geographic location, etc.), or are associated with a behavior:purchases, TV viewership, site visits, etc.

Cookie syncing refers to a process that allows data exchange betweenDMP's SSP's and DSP's, and more generally between publishers of contentand advertisement buyers. A cookie is a file that a mobile device ordesktop computer uses to retain and restore information about aparticular user or device. The information in a cookie is typicallyprotected so that only an entity that created the cookie cansubsequently retrieve the information from it. Cookie syncing is a wayin which one entity can obtain information about a consumer from thecookie created by another entity, without necessarily obtaining theexact identify of the consumer. Thus, given information about aparticular consumer received from a media conduit, through cookiesyncing it is possible to add further information about that consumerfrom a DMP.

For mobile devices, there is a device ID, unique to a particular device.For TV's there is a hashed IP address. The device ID information may beused to link a group of devices to a particular consumer, as well aslink a number of consumers, for example in a given household, to aparticular device. A DSP may gather a store of data, built up over time,in conjunction with mobile device ID's and TV addresses, that augment‘cookie’ data.

Cross-screen refers to distribution of media data, including advertisingcontent, across multiple devices of a given consumer, such as a TVscreen, computer screen, or display of a mobile device such as a tablet,smart-phone or laptop/notebook computer, a wearable such as asmart-watch or fitness monitor, or an in-car, or in-plane displayscreen.

Reach is the total number of different people exposed to anadvertisement, at least once, during a given period.

In a cross-screen advertising or media campaign, the same consumer canbe exposed to an advertisement multiple times, through different devices(such as TV, desktop or mobile) that the consumer uses. Deduplicatedreach is the number of different people exposed to an advertisementirrespective of the device. For example, if a particular consumer hasseen an advertisement on his/her TV, desktop and one or more mobiledevices, that consumer only contributes 1 to the reach.

The incremental reach is the additional deduplicated reach for acampaign, over and above the reach achieved before starting a givencampaign, such as from a prior campaign. In one embodiment herein, atype of campaign can include a TV extension: in this circumstance, anadvertiser has already run a campaign on TV, but is reaching a point ofdiminished returns. The advertiser wants to find ways to modify thecampaign plan for a digital market, in order to increase the reach. Inthis way, a DSP may inherit a campaign that has already run its courseon one or more media conduits.

In addition to TV programming content, and online content delivered todesktop computers and mobile devices, advertisements may be deliveredwithin OTT content. OTT (which derives from the term “over the top”)refers to the delivery of audio, and video, over the Internet withoutthe involvement of a MVPD in the control or distribution of the content.Thus, OTT content is anything not tied to particular box or device. Forexample, Netflix, or HBO-Go, deliver OTT content because a consumerdoesn't need a specific device to view the content. By contrast, MVPDcontent such as delivered to a cable or set top box is controlled by acable or satellite provider such as Comcast, AT&T or DirecTV, and is notdescribed as OTT. OTT in particular refers to content that arrives froma third party, such as Sling TV, YuppTV, Amazon Instant Video, Mobibase,Dramatize, Presto, DramaFever, Crackle, HBO, Hulu, myTV, Netflix, NowTV, Qello, RPI TV, Viewster, WhereverTV, Crunchyroll or WWE Network, andis delivered to an end-user device, leaving the Internet serviceprovider (ISP) with only the role of transporting IP packets.

Furthermore, an OTT device is any device that is connected to theinternet and that can access a multitude of content. For example, Xbox,Roku, Tivo, Hulu (and other devices that can run on top of cable), adesktop computer, and a smart TV, are examples of OTT devices.

Gross rating point (GRP) refers to the size of an advertising campaignaccording to schedule and media conduits involved, and is given by thenumber of impressions per member of the target audience, expressed as apercentage (GRP can therefore be a number >100. For example, if anadvert reaches 30% of the population of L.A. 4 times, the GRP is 120.(The data may be measured by, e.g., a Nielsen panel of say 1,000 viewersin L.A.).

The target rating point (TRP) refers to the number of impressions pertarget audience member, based on a sample population. This numberrelates to individuals: e.g., within L.A. the advertiser wants to targetmales, 25 and older. If there are 100 such persons in the L.A. panel and70% saw the ad., then the TRP is 70% X number of views.

Real-time refers to real-time computing, and is defined as a computingsystem that can receive and process data, and return analyzed resultssufficiently rapidly (such as within a matter of seconds) that iteffectively does not cause delay to a party who relies upon the resultsfor decision-making purposes. It is to be assumed that the processes forallowing an advertiser to select, bid on, and purchase advertisinginventory, as described herein, can be carried out in real-time.

At various stages of the methods herein, it is described that eachconsumer in a population of consumers is treated in a particular way bythe method: for example, a computer may be programmed to analyze data oneach consumer in its database in order to ascertain which, if any, haveviewed a particular TV show, or visited a particular website;alternatively, some comparative analysis may be performed, in whichattributes of each user in one category of population is compared withattributes of each consumer in another category of population. Eachpopulation set may comprise many thousands of individuals, or manyhundreds of thousands, or even millions or many millions of individuals.It is assumed herein that the methods, when deployed on suitablecomputing resources, are capable of carrying out stated calculations andmanipulations on each and every member of the populations in question.However, it is also consistent with the methods herein that “eachconsumer” in a population may also mean most consumers in thepopulation, or all consumers in the population for whom the statedcalculation is feasible. For example, where one or more given consumersin a population is omitted from a particular calculation because thereis insufficient data on the individual, that does not mean that aninsufficient number of members of the population is analyzed in order toprovide a meaningful outcome of the calculation. Thus “each” whenreferencing a population of potentially millions of consumers does notnecessarily mean exactly every member of the population but may mean alarge and practically reasonable number of members of the population,which hfor the purposes of a given calculation is sufficient.

Consumer Graph

A consumer graph is a graph in which each node represents a consumer (orindividual user). The technology utilizes various implementations of aweighted graph representation in which relationships between consumers(nodes) are defined as degrees of similarity (edges). A consumer graphis used herein to categorize, store, and aggregate large amounts ofconsumer data, and allow an entity such as a DSP to make connectionsbetween data used to build a consumer graph with other data—such as TVviewing data—via data on given consumers' devices.

One way to construct the graph is by using deterministic relationshipdata; another is probabilistically using the attributes of each node. Insome instances, a combination of deterministic and probabilistic methodscan be used. In a deterministic, approach, which is relativelystraightforward, the basis is having exact data on a consumer, such aslogin information from a publisher. Thus, if a person has logged inmultiple times on different devices with the same ID, then it ispossible to be sure that the person's identity is matched. However, suchexact information may not always be available. By contrast, in aprobabilistic approach, it is necessary to draw inferences: for example,if the same device is seen in the same location, or similar behavior canbe attributed to a given device at different times, then it possible toconclude that the device belongs to the same user.

In preferred embodiments, the device graph herein is based onprobabilistic data. The probabilistic approach to graph constructionuses behavioral data to match up users. VA creates a profile based onviewership habits, etc.

In some embodiments, an entity such as a DSP, can construct a devicegraph; in other embodiments it can obtain, such as purchase, it from aanother entity such as a DMP.

In various embodiments herein, both a device graph and a consumer graphare operating together in a manner that permits tying in mobile data toTV data.

The term graph is used herein in its mathematical sense, as a set G(N,E) of nodes (N) and edges (E) connecting pairs of nodes. Graph G is arepresentation of the relationships between the nodes: two nodes thatare connected by an edge are similar to one another according to somecriterion, and the weight of an edge defines the strength of thesimilarity. Pairs of nodes that do not meet the similarity criterion arenot joined by an edge. FIG. 2 illustrates graph concepts, showing 6nodes, N₁-N₆, in which three pairs of nodes are connected by edges.

In the implementation of a graph herein, a node, N, is an entity orobject with a collection of attributes, A. In FIG. 2, each node hasassociated with it an array of attributes, denoted Ai for node Ni.

In the implementation of a graph herein, an edge, E, existing betweentwo nodes indicates the existence of a relationship, or level ofsimilarity, between the two nodes that is above a defined threshold. Theweight of an edge, w_E, is the degree of similarity of the two nodes.The weights of the edges in FIG. 2 are shown diagrammatically asthicknesses (in which case, w_E₁₂>w_E₃₄>w_E₁₅).

In a consumer graph, a node represents an individual, or a householdcomprising two or more individuals, with a set of attributes such as thegender(s) and age(s) of the individual(s), history of TV programswatched, web-sites visited, etc.

FIG. 3 illustrates an exemplary structure of a node of a consumer graph.Each node has a collection of attributes that include types andbehaviors, for which data is continuously collected from first party andthird party sources. An aspect of the technology herein is that thegraph is constructed from a potentially unlimited number of inputs for agiven consumer, such as online, offline, behavioral, and demographicdata. Those inputs are updated over time and allow the data for a givenconsumer to be refined, as well as allow the population of consumers onwhich data can be used to be expanded. The fact that there is no limitto the type and character of data that can be employed means that themethods herein are superior to those employed by panel companies, whichrely on static datasets and fixed populations.

A first party is the entity that is constructing the graph, for examplea DSP. This entity is receiving, gathering, storing, and accumulating,as well as analyzing consumer data. Over time, it will be able to usethe data it has accumulated in addition to working with data from otherparties. First party data includes data that depends on the party havingserved an advert in order to have access to it. A third party is anotherparty, such as a DMP, that sends data to the entity constructing thegraph.

Many of the attributes of a consumer are transmutable if new informationfor the consumer becomes available, and the collection of attributes(i.e., the number of different attributes stored for a given consumer)can also grow over time as new data is collected about the consumer.Some of the sources from which data is collected are as follows.

Type data is categorical data about a consumer that normally does notchange, i.e., is immutable. Behavioral data is continuously updatedbased on a consumer's recent activity.

Each node includes a grouping of one or more devices (desktop, mobile,tablets, smart TV). For each device, data on the type of the user basedon the device is collected from third party and first party sources.

TABLE 1 1^(st) Party 3^(rd) Party Non-transmutable Census (Govt.)Household income Education Level (e.g., from Exelate) Gender (e.g., fromNielsen, DAR) Transmutable Behavior (online) Offline Behavior TV viewingRetail Purchases Viewability (how much Offsite visits (visited of advertseen, kept on, pharmacy, movie theater, visible online?) car dealership,etc.) Online sites visited Location events

Table 1 shows examples of data by category and source.

First party data comprises data on a user's behavior, for example:purchases, viewership, site visits, etc., as well as types such asincome, gender, provided directly by a publisher to improve targetingand reporting on their own campaigns. (For example, the Coca Colacompany might provide to a DSP, a list of users who “like” Coke productson social media to improve their video advertising campaigns.) Firstparty type data can be collected from advertisements served directly tothe device, and from information collected from the device, such as oneor more IP addresses. First party type data includes location from IPaddress, geolocation from mobile devices, and whether the device islocated in a commercial or residential property.

Third party type data is obtained from external vendors. Through aone-on-one cookie synchronization or a device synchronization, anexternal vendor, for example a DMP such as Krux (www.krux.com/),Experian (which provides purchase behavior data), or Adobe, providesinformation about the cookie or device. Example data includes a marketsegment occupied by the consumer, such as age range, gender, incomelevel, education level, political affiliation, and preferences such aswhich brands the consumer likes or follows on social media.Additionally, external vendors can provide type data based on recentpurchases attributed to the device. Third party data includesinformation such as gender and income because it is collected directlyfrom external vendors. Third party data can be collected without servingan advertisement. TV programs viewed and purchased are third party data.

First party data is typically generated by a DSP; for example, it isdata that the DSP can collect from serving an advertisement or obtainfrom a brand/agency that provides the data. First party data includesdata that depends on having served an advertisement to have access toit.

Behavioral data can be collected from the devices through first partyand third party sources. Behaviors are first party data typically, andare mutable.

First party behavioral data is collected from advertisements serveddirectly to the device. This includes websites visited, and the TVprogram, or OTT, or video on demand (VOD) content viewed by the device.

Third party behavioral data is obtained from external vendors, typicallyDMP's such as Experian, Krux (www.krux.com/), Adobe, Nielsen andComscore, and advertising exchanges or networks, such as Brightroll,SpotX, FreeWheel, Hulu. Example data includes the history of TVprogramming viewed on the device in the last month, the history ofwebsites visited by a personal computer or laptop, or mobile device, andhistory of location based events from mobile devices (for example,whether the device was at a Starbucks). In some instances, the sametypes of data can be obtained from both first party and third partyentities.

Edges between the nodes in the consumer graph signify that the consumershave a threshold similarity, or interact with each other. The edges canbe calculated deterministically, for example, if the nodes are inphysical proximity, or probabilistically based on similarity inattributes. Probabilistic methods utilized include, but are not limitedto: K-means clustering, and connected components analysis (which isbased on graph traversal methods involving constructing a path acrossthe graph, from one vertex to another. Since the attributes aretransmutable, the edges can also change, either in their weighting or bybeing created or abolished if the similarity score for a pair of nodesalters. Thus the graph is not static, and can change over time. In someembodiments, change is dynamic: similarity scores are continuallyrecalculated as attributes for nodes are updated.

In some embodiments, aspects of machine learning are used to calculateedges, or contributions to edges, between the nodes in the consumergraph.

In some embodiments, aspects of deep learning are used to calculateedges, or contributions to edges, between the nodes in the consumergraph: for example, using techniques of deep learning, it is possible tofind commonalities between videos liked by a consumer based onfine-scale content of the videos such as characters or scenes portrayed,rather than base commonalities on categorical data such as author,title, or keywords relating to subject matter of the content.

Typically, attributes and data are added dynamically (as they areobtained). The graph may be re-constructed weekly to take account of thenew attributes and data, thereby establishing new weightings for theedges, and identifying newly connected or reconnected devices. (Graphconstruction and reconstruction may be done in the cloud, or on adatacenter under the control of the DSP.)

The similarity, S, between two nodes N_1, N_2, is calculated accordingto a similarity metric, which is the inverse of a distance function,f(N_1, N_2):N_1, N_2→S, that defines the similarity of two nodes basedon their attributes.

In a consumer graph, similarity represents the likeness of twoindividuals in terms of their demographic attributes and their viewingpreferences. Similarities can be calculated, attribute by attribute, andthen the individual similarity attributes weighted and combined togetherto produce an overall similarity score for a pair of nodes.

When the attributes of two nodes are represented by binary vectors,there are a number of metrics that can be used to define a similaritybetween a pair of nodes based on that attribute. Any one of thesemetrics is suitable for use with the technology herein. In someembodiments, for efficiency of storage, a binary vector can berepresented as a bit-string, or an array of bit-strings.

When working with a similarity metric that is the inverse of a distancefunction, f(N_i, N_j), a zero value of the distance function signifiesthat the types and behaviors of the two nodes are identical. Conversely,a large value of the distance function signifies that the two nodes aredissimilar. An example of a distance function is Euclidean distance,

f(N_i, N_j)=∥A_i−A_j∥̂2

where A_i, and A_j are the sparse vectors representing the attributes ofnodes N_i and N_j, and the distance is computed as a sum of the squaresof the differences of in the values of corresponding components of eachvector.

Comparisons of binary vectors or bit-strings can be accomplishedaccording to one or more of several similarity metrics, of which themost popular is the Tanimoto coefficient. Other popular metrics include,but are not limited to: Cosine, Dice, Euclidean, Manhattan, city block,Euclidean, Hamming, and Tversky. Another distance metric that can beused is the LDA (latent Dirichlet allocation). Another way of defining adistance comparison is via a deep learning embedding, in which it ispossible to learn the best form of the distance metric instead of fixingit as, e.g., the cosine distance. An example approach is via manifoldlearning.

The cosine dot product is a preferred metric that can be used to definea similarity between the two nodes in a consumer graph. The cosinesimilarity, that is the dot product of A_i and A_j, is given by:

f(N_i, N_j)=A_i.A_j

In this instance, the vectors are each normalized so that theirmagnitudes are 1.0. A value of 1.0 for the cosine similarity metricindicates two nodes that are identical. Conversely, the nearer to 0.0 isthe value of the cosine metric, the more dissimilar are the two nodes.The cosine metric can be converted into a distance-like quantity bysubtracting its value from 1.0:

f′(N_i, N_j)=1−A_i.A_j

An example of a more complex distance function is a parameterizedKernel, such as a radial basis function:

f(N_i, N_j)=exp(∥A_i−A_j∥̂2/ŝ2),

where s is a parameter.

In the more general case in which the bit-string is a vector thatcontains numbers other than 1 and 0 (for example it contains percentagesor non-normalized data), then one can calculate similarity based ondistance metrics between vectors of numbers. Other metrics, such as theMahalanobis distance, may then be applicable.

Typically, a similarity score, S, is a number between 0 and 100, thoughother normalization schemes could be used, such as a number between 0and 1.0, a number between 0 and 10, or a number between 0 and 1,000. Itis also possible that a scoring system could be un-normalized, andsimply be expressed as a number proportional to the calculatedsimilarity between two consumers.

In some embodiments, when calculating a similarity score, eachcontributing factor can be weighted by a coefficient that expresses therelative importance of the factor. For example, a person's gender can begiven a higher weighting than whether they watched a particular TV show.The weightings can be initially set by application of heuristics, andcan ultimately be derived from a statistical analysis of advertisingcampaign efficacy that is continually updated over time. Other methodsof deriving a weighting coefficient used to determine the contributionof a particular attribute to the similarity score include: regression,or feature selection such as least absolute shrinkage and selectionoperator (“LASSO”). Alternatively, it is possible to fit to “groundtruth data”, e.g., login data. In some embodiments, as the system triesdifferent combinations or features, which one leads to greaterprecision/recall can be deduced by using a “held out” test data set(where that feature is not used in construction of the graph).

Another way of deriving a similarity score for a feature is to analyzedata from a successive comparison of advertising campaigns to consumerfeedback using a method selected from: machine learning; neural networksand other multi-layer perceptrons; support vector machines; principalcomponents analysis; Bayesian classifiers; Fisher Discriminants; LinearDiscriminants; Maximum Likelihood Estimation; Least squares estimation;Logistic Regressions; Gaussian Mixture Models; Genetic Algorithms;Simulated Annealing; Decision Trees; Projective Likelihood; k-NearestNeighbor; Function Discriminant Analysis; Predictive Learning via RuleEnsembles; Natural Language Processing, State Machines; Rule Systems;Probabilistic Models; Expectation-Maximization; and Hidden and maximumentropy Markov models. Each of these methods can assess the relevance ofa given attribute of a consumer for purposes of suitability formeasuring effectiveness of an advertising campaign, and provide aquantitative weighting of each.

Representation

To properly assess an entire population of consumers, a large number ofnodes needs to be stored. Additionally, the collection of attributesthat represent a node's types and behaviors can be sizeable. Storing thecollection of the large number of attributes for the nodes ischallenging, since the number of nodes is in the hundreds of millions.Storing the data efficiently is also important since the graphcomputations can be done most quickly and efficiently if the node datais stored in memory.

In a preferred embodiment, attributes are represented by sparse vectors.In order to accomplish such a representation, the union of all possiblenode attributes for a given type is stored in a dictionary. Then thetype, or behavior, for each node is represented as a binary sparsevector, where 1 and 0 represent the presence and absence of anattribute, respectively. Since the number of possible attributes of agiven type is very large, most of the entries will be 0 for a givenconsumer. Thus it is only necessary to store the addresses of thoseattributes that are non zero, and each sparse vector can be storedefficiently, typically in less than 1/100^(th) of the space that wouldbe occupied by the full vector.

As an example, let the attributes encode the TV programs that a givenconsumer has viewed in the last month. The system enumerates allpossible TV shows in the dictionary, which can be up to 100,000different shows. For each node, whether the consumer watched the show inthe last month is indicated with a 1, and a 0 otherwise.

If the attributes indicate different income levels, multiple incomelevels are enumerated, and a 1 represents that the consumer belongs to aparticular income level (and all other entries are 0).

Thus for a consumer, i, having an annual income in the range$30,000-$60,000, and who has viewed the TV program “Top Gear” in thelast month, the following is established:

TV_Dictionary={“Walking Dead”, “Game of Thrones”, . . . , “TopGear”}TV_i=[0, 0, . . . , 1]

TV_i can be stored as simply [4]; only the 4^(th) element of the vectoris non-zero. Similarly, for income:

Income_Dictionary={<$30,000, $30,000-$60,000,$60,000-$100,000, >$100,000} Income_i=[0, 1, 0, 0]

Income_i can be stored as simply [2], as only the second element of thevector is non-zero.

All the attributes of a node, i, can thus be efficiently representedwith sparse vectors. This requires 2 to 3 orders of magnitude lessmemory than a dense representation.

Graph Construction

FIGS. 4A and 4B illustrate a flow-chart for steps in construction of aconsumer graph.

Initially, the graph is a collection of devices, which are mapped toconsumers. Multiple data sources are used to group multiple devices(tablet, mobile, TV, etc.) to a single consumer. This typically utilizesagglomerative techniques. In order to attribute a single device (e.g., aSmart TV) to multiple consumers, a refinement technique is used.

With agglomerative methods, multiple devices can be grouped to a singleconsumer (or graph node). Some data sources used for this include, butare not limited to:

-   -   IP addresses: multiple devices belonging to same IP address        indicates a single consumer or a household.    -   Geolocation: multiple devices that are nearby, using latitude        and longitude, can be attributed to a single consumer.    -   Publisher logins: if the same consumer is logged in from        multiple devices, those devices can be associated with that        consumer.

During this process, the consumer's identity is masked, to obviateprivacy concerns. The result is a single consumer ID that linksparticular devices together.

Let P(d_i, d_j) be the probability that the two devices, d_i and d_j,belong to the same node (consumer, or household). From multiple datasetsobtained from different categories of device, it is possible toconstruct the probability:

P(d_i, d_j)=w_IP×P(d_i, d_j|IP)×w_Geo×P(d_i, d_j|Geo)×w_Login×P(d_i,d_j|Login)/Z

where “X” means “multiply”, where w_are weighting factors, P(d_i, d_j|Y)is a conditional probability (the probability of observing device i anddevice j belong to same user, if Y has the same value for both, and Z isa normalizing factor. Thus, Y may be an IP address. (The value of theconditional probability may be 0.80). Each data source gets a differentweighing factor: for example, login data can be weighted higher than IPaddresses. The weights can be fixed, or learned from an independentvalidation dataset.

Once multiple devices are grouped to a single node, the types andbehaviors from the respective devices are aggregated to the singularnode's attributes. For example, attributes (and the corresponding sparsevectors) from mobile (such as location events), and desktop (recentpurchases) are aggregated. This provides more comprehensive informationfor a consumer, permitting more accurate and meaningful inferences for anode to be made.

Associating a device with a given consumer is possible due to the datathat is associated with those devices and known to various mediaconduits. For example, a Smart-TV stores location information as well assubscription information about the content broadcast by it. Thisinformation is shared with, and can be obtained from, other entitiessuch as a cable company. Similarly, a mobile device such as a tablet orsmartphone may be associated with the same (in-home) wifi network as theSmart-TV. Information about the location is therefore shared with, e.g.,the cell-phone carrier, as well as broadcasters of subscription contentto the mobile device. A key aspect of the graph methodology herein isthat it permits consumer information to be linked across differentdevice and media platforms that have typically been segregated from oneanother: in particular, the graph herein is able to link consumer datafrom online and offline purchasing and viewing sources with TV viewingdata.

With refinement methods, a single device (for example, a smart TV) canbe associated with multiple consumers (or graph nodes) who, for example,own mobile devices that are connected to the same wifi network as thesmart-TV.

Given a node, n, to which are assigned multiple devices, the variousattributes are clustered into smaller groups of devices, for example, aTV ID, connected to multiple devices from a common IP address. The TVviewership data is aggregated along with the attributes from all thedevices. A clustering algorithm, such as k-means clustering, can beapplied to group the devices into smaller clusters. The number ofclusters, k, can be set generally by the number of devices (by defaultk=# number of devices/4). Sometimes it is possible to only collectaggregate data at a household level. For example, there may be as manyas 20 devices in one household. But by using behavioral data, it can beascertained that the 20 devices have 4 major clusters, say with 5devices each, where the clusters correspond to different individualswithin the same household. Thus, although there are two categories ofdevice (shared and personal), it is still important to attributebehavioral data to users.

Once a shared device is attributed to multiple nodes, the data collectedfrom the device can be attributed to the nodes. For example, TV viewingdata from a Smart TV can be collected from the OEM. Through thisattribution, the TV viewing data can be added to the collection of anode's attributes. Ultimately, a Smart-TV can be attributed to differentpersons in the same household.

Lookalike Modeling by Learning Distance Functions

Given a graph, G(N, E), and a functional form that defines a similaritymetric, and a set of seed nodes, it is possible to generate a set of“lookalike” nodes that are similar to the seed nodes, where similarityis defined by a function that is fixed, or learned. This is useful whenidentifying new consumers who may be interested in the same or similarcontent as a group of consumers already known to an advertiser. This isshown schematically in FIG. 5, wherein, for a population of consumers500 seed nodes 502 etc. are shaded, and lookalike nodes 504 etc. areunshaded. Similar principles can be utilized when projecting likelyviewing behavior of consumers from historical data on a population ofconsumers.

Seed nodes can be a set of nodes, e.g., household(s) or individual(s),from which to generate a set of lookalike nodes using a fixed, orlearned, similarity metric. For example, seed nodes can be defined as anaudience segment (such as list of users that saw a specific show forcertain). This is useful for determining, for each member of theaudience segment, a list of other audience members who might havesimilar viewing habits even if they did not watch exactly the same showas the seeds.

Given the set of seed nodes in a graph (and their attributes), theoutput of lookalike modeling is a set of nodes (that includes the seednodes) that are similar to the seed nodes based on the fixed or learnedsimilarity metric.

Several different vectors can be used in determining look-alike models:One is the vector of TV programs in total. This vector can be as long as40k elements. Another vector is the list of consumers who saw aparticular program (e.g., The Simpsons). The vector of viewers for agiven TV program can be as long as 10M elements, because it contains oneelement per consumer. Another vector would be a vector of web-sitesvisited (say 100k elements long). Still another vector would be based ononline videos viewed (which can also be 100k elements long).

In general, TV program comparison data accesses a 10M user base. Onlinedata can identify a potentially much larger audience, such as 150Mconsumers. It should be understood that TV data can be accumulatedacross a variety of TV consumption devices that include, but are notlimited to linear, time-shifted, traditional and programmatic.

The similarity between 2 distinct nodes can be calculated from theirattributes, represented by sparse vectors. Given a distance functionf(N_i, N_j), and a set of seed nodes, N_S, the pairwise distancesbetween each element of the seed nodes, n in N_S, and all other nodesother than the seed node, n′, are calculated. That is, all quantitiesf(n, n′) are calculated.

After calculating all pairwise similarities, only the nodes such thatf(n, n′)<T are selected. T is a threshold maximum distance below whichthe nodes are deemed to be similar. Alternatively, values of f(n, n′)(where n is not n′) are ranked in decreasing order, and the top t nodepairs are selected. In either case, T and t are parameters that arepreset (provided to the method), or learned from ground truth orvalidation data. The set of all nodes n′ that satisfy the criteriaabove, form the set of “lookalike nodes”.

Graph Inference

Given a graph G(N, E), it is also possible to infer likely attributes ofa node, n, based on the attributes of its neighbors in the graph. Thiscan be useful when incomplete information exists for a given consumerbut where enough exists from which inferences can be drawn. For example,TV viewership attributes may be missing for a node n (in general, thereis either positive information if a user did watch a show, or it isunknown whether they watched it), whereas those attributes are availablefor neighbor nodes n′, n″ in the graph. Nodes n, n′, and n″ contain allother attributes, such as income level and websites visited.

In another example, it can be useful to calculate the probability thatthe consumer associated with node n would watch the show “Walking Dead”,given that n′, n″ both also watch “Walking Dead”. If the similarity,given by the weight of the edges between n and n′, n″, are w′, w″=0.8and 0.9 respectively, and the likelihood of n watching the show based onhis/her own attributes is 0.9, then the probability is given by:

${P\left( {n\mspace{14mu} {watches}\mspace{14mu} {``{{Walking}\mspace{14mu} {Dead}}"}} \right)} = {\left\lbrack {{0.8 \times 0.9} + {0.9 \times 0.9}} \right\rbrack/{\quad{\left\lbrack {{0.8 \times 0.9} + {0.9 \times 0.9} + \left( {1 - {0.8 \times 0.9}} \right) + \left( {1 - {0.9 \times 0.9}} \right)} \right\rbrack = 0.765}}}$

Similar principles can be utilized when projecting likely viewingbehavior of consumers from historical data on a population of consumers.

Accuracy

The graph is continually refined as new data is received. In oneembodiment, a technique such as machine learning is used to improve thequality of graph over time. This may be done at periodic intervals, forexample at a weekly build stage. It is consistent with the methodsherein that the graph utilized is updated frequently as new consumerdata becomes available.

To determine the accuracy of a graph, the precision and recall can becompared against a validation dataset. The validation dataset istypically a (sub)graph where the device and node relationships are knownwith certainty. For example, the login information from an onlinenetwork such as eHarmony, indicates when the same user has logged intothe site from different desktops (office, laptop), and mobile devices(smartphone and tablet). All the devices that are frequently used tologin to the site are thus tied to the same consumer and thereby thatindividual's graph node. This information can be used to validatewhether the constructed graph ties those devices to the same node.

If D is the set of devices in the validation set, let Z(D) denote thegraph, consisting of a set of nodes, constructed from the set ofdevices, D. For different datasets, and different graph constructionmethods, it is possible to obtain different results for Z(D).

For the set Z(D), true positive (TP), false positive (FP), and falsenegative (FN) rates can all be calculated. True positives are all nodesin Z(D) that are also nodes in the validation set. False positives areall nodes in N(D) that do not belong to the set of nodes in thevalidation set. False negatives are all nodes that belong to thevalidation set, but do not belong to Z(D).

Precision, defined as TP/(TP+FP), is the fraction of retrieved devicesthat are correctly grouped as consumer nodes.

Recall, defined as TP/(TP+FN), is the fraction of the consumer nodesthat are correctly grouped.

Depending on the application at hand, there are different tradeoffsbetween precision and recall. In the case of constructing a consumergraph, it is preferable to obtain both high precision and high recallrates that can be used to compare different consumer graphs.

The validation dataset must not have been used in the construction ofthe graph itself because, by doing so, bias is introduced into theprecision and recall values.

Learning the Similarity Metric

Another feature of the graph that can be adjusted as more data isintroduced is the underlying similarity metric. Typically, the metric isfixed for long periods of time, say 5-10 iterations of the graph, andthe metric is not reassessed at the same frequency as the accuracy.

In the case where the distance function is not fixed, it is possible tolearn the parameters of a particular distance function, or to choose thebest distance function from a family of such functions. In order tolearn the distance function or its parameters, the values of precisionand recall are compared against a validation set.

Suppose a goal is to predict the lookalike audience segment that arehigh income earners, based on the attributes of a seed set of known highincome earners. The similarity of the seed nodes to all other nodes inthe graph is calculated for different distance functions, or parametersof a particular distance function. The distance function uses theattributes of the nodes, such as online and TV viewership, to calculatethe similarities.

For example, if the distance function is the radial basis function withparameter, s:

f(N_i, N_j)=exp(∥A_i−A_j∥̂2/ŝ2),

then the pairwise distances from the seed nodes to all other nodes, arecalculated for different values of s, using the same threshold distancevalue, T, to generate the set of lookalike nodes. For different valuesof s (the parameter that needs to be learned), the calculations producedifferent sets of lookalike nodes, denoted by N_S(s).

For the set N_S(s), it is possible to calculate true positive (TP),false positive (FP) and false negative (FN) rates. True positives areall nodes in N_S(s) that also belong to the target set in the validationset. In this example, all the nodes that are also high income earners(in ground truth set). False positives are all nodes in N_S(s) that donot belong to the target set (not high income earners). False positivesare all nodes in N_S(s) that do not belong to the target set (not highincome earners). False negatives are all nodes that belong to thevalidation set (are high income earners), but do not belong to N_S(s).

Based on the application, it is possible to require different tradeoffsbetween precision and recall. In the case of targeting an audience withan advertisement, a high recall rate is desired, since the cost ofexposure (an advertisement) is low, whereas the cost of missing a memberof a targeted audience is high.

In the example herein, the aim is to choose the value of s for whichboth the precision and recall rates are high from amongst possiblevalues of s. For other types of distance function, there may be otherparameters for which to try to maximize the precision and recall rates.

The accuracy of a lookalike model can only be defined for a targetaudience segment. For example, it is possible to predict whether alookalike segment also comprises high income earners, from a seed set ofhigh income earners using TV viewing and online behavior datasets.Predictions can be validated using a true set of income levels for thepredicted set of nodes. This gives the accuracy of the predictions.However, the accuracy of predictions for one segment are not meaningfulfor a new target segment, such as whether those same users are alsoluxury car drivers.

Calculating Deduplicated Reach

The consumer graph connects a node (consumer) to all the devices that heor she uses. Thus the graph enables deduplicating the total exposure toan advertisement, to individuals. Deduplicatd reach is an importantconcept for advertisers for whom it is more important to be sure that alarge number of different individuals have seen the advertising contentthan that a smaller number saw the content more than once on differentdevices. For example, if user abc123 has already seen a particularadvertisement on each of his TV, desktop and mobile device, the totaldeduplicated exposures will count as 1. This enables the calculation ofthe following metrics for direct measurement.

The deduplicated exposed audience is the number of users belonging tothe target audience segment in the consumer graph who were exposed tothe advertisement after deduplication. Then, the direct deduplicatedreach is:

Deduplicated Reach=Deduplicated Exposed Audience/Total Audience

For sampled measurement, this enables the calculation of thededuplicated exposed sampled audience as the number of sampled users whobelong to the target audience segment who were exposed to theadvertisement after deduplication. Then, the sampled reach is:

Deduplicated Sampled Reach=Deduplicated Exposed Sampled Audience/TotalSampled Audience

In the case of modeled measurement data, the ID of the user in theconsumer graph from whom the data was collected is not known. Hence, thereach data cannot be deduplicated on a one-to-one level.

Calculation of deduplicated reach can be useful in sequential targetingof advertising content, if an advertiser wants to impose a frequency capon consumers (for example, if the advertiser doesn't want to show thesame advertisement to the same user more than twice). Deduplicated reachalso provides a convenient metric by which to optimize the efficacy ofan advertising campaign: for example, by calculating the deduplicatedreach over time, as an advertising campaign is adjusted, improvementscan continue to be made by altering parameters of the campaign such as,for example, consumer demographic, or time and channel of broadcast ofTV content.

Calculating Incremental Reach

On day t, let the deduplicated reach (direct or sampled) be x. Theincremental reach is the additional deduplicated reach after running thecampaign. In a cross-screen environment, this is a useful parameter tocalculate if an advertiser wants be able to assess whether they canextend a 30% reach via TV to say, a 35% reach by extending to mobileplatforms. One caveat is that in direct measurement of, e.g., TV data,the portion of the sample obtained for smart-TV's is only a subset ofthe overall data, due to the relatively small number of smart-TV'scurrently in the population at large.

The ability to calculate an incremental reach provides a significantadvantage over panel data, for whom extrapolations need to be made. Inthe case of modeled measurement data such as is obtained from a panelwhere the nature of the sample has to be inferred, the ID of the user inthe consumer graph from whom the data was collected is not known. Hence,it is not possible to tell if the same user has viewed the advertisementin the past. Therefore the incremental deduplicated reach cannot becalculated for modeled data because devices cannot be associated withparticular users. However, the incremental reach from the sampled]measurement, without deduplication, can be calculated.

First Party Audience Measurement

The consumer graph enables the calculation of the Total Reach, combinedfrom direct and sampled data, for a First Party audience segment. Thefirst party audience segment is the target audience composed ofconsumers in the graph. For example, a publisher like Truecar.com orESPN.com can provide a list of all visitors to their website. Thevisitors are mapped to nodes of the graph. Furthermore, the subset ofnodes from which sampled data can be collected can be identified. Thefollowing quantities can be calculated from the intersection ofconsumers (nodes) that are in the total and sampled dataset. The TotalFirst Party Reach is given by:

Deduplicated First Party Exposed Audience=Deduplicated ExposedAudience+Deduplicated Exposed Sampled Audience−Number of ad exposures ondevices common to both graph and sampled dataset.

That is, the exposed audience numbers from direct and sampledmeasurements are combined, and the devices common to the two datasetsare subtracted from the result.

Deduplicated First Party Reach=Deduplicated First Party ExposedAudience/Total Audience.

Thus the consumer graph combined with direct and sampled data allowscalculation of reach and frequency against first party data, whichextends measurement beyond demographic segments (such as age, gender,and income) that measurement solutions like Nielsen and Rentrak/Comscoretypically provide.

Sequential Delivery of Content

The technology described herein permits an advertiser to targetadvertising content to a consumer across more than one media conduit,including both TV and online media. The advertiser has control over theallocation of the advertising content because the advertiser accessesthe system via a unified interface that presents information aboutinventory, manages bids on the inventory, and provides a list ofpotential advertising targets consistent with a campaign description andthe advertiser's budget. The system then communicates with, for example,supply-side providers to ensure that the desired slots are purchased andthe advertising content is delivered.

In one embodiment, the technology provides for an advertising campaignthat is based on telling a story via sequential delivery of contentacross two or more media conduits, rather than delivery of a singleadvertisement to multiple consumers at different times on, say, TV only.The system thereby permits delivery of advertising content to a givenconsumer over two or more sessions on more than one device. For example,a consumer begins watching a campaign on their cell phone, and then maycontinue by viewing the second portion of the campaign on a TV, and thenconclude by viewing a third portion of the campaign within a desktopbrowser session on their laptop or on their OTT device. This permits theadvertiser to tell a more elaborate story than is possible within asingle 30 second video clip, because the advertiser is not limited todelivering all its relevant content within a single session, or overmultiple potentially duplicative sessions. The advertiser can thereforeuse the system to create a seamless story telling experience for atarget audience.

There are two aspects of the technology that enable an advertiser tosuccessfully manage sequential delivery of content: the system is ableto keep track of which devices a given consumer can access, as well ason which devices the user has seen which portions of the advertisingnarrative; the system can also identify those consumers that are mostlikely to be interested in the sequentially delivered content. Accuracyin sequential targeting can thus be achieved via predictions based on amapping of consumer behavior from aggregated cross-screen viewershipdata.

The analytics portion of the system is capable of accepting unlimiteddata inputs regarding consumer behavior across various media, includingbut not limited to behavioral such as specific viewing and purchasinghistories of individual consumers, as well as demographic, andlocation-related sources. The system uses this data to optimize consumerclassifications. A second part of the output is improved measurement andprediction of future consumer behaviors based on the data oncross-screen behavior.

Analysis of cross-screen data is able to determine where and when aconsumer has viewed an advertisement, or a particular version of it, andthereby permits advertisers to schedule sequential viewing of anadvertising campaign. Advertisers can then schedule where, when, and howan advertisement is subsequently broadcast. They have control overretargeting (whether they show the same advertising more than once), orcan choose to broadcast a multi-chapter advertising story. This isachieved via a feedback data loop that informs the system when aparticular consumer has viewed the advertisement in real-time.

One method for delivering advertising content sequentially to a consumeris illustrated in FIGS. 6A and 6B. The system receives a pricepoint 602one or more campaign descriptions 600 from an advertiser, wherein eachof the campaign descriptions 600 comprises a schedule for sequentialdelivery of one or more items of advertising content across one or moredevices accessed by a consumer, and a target audience, wherein thetarget audience is defined by one or more demographic factors selectedfrom: age range, gender, and location. A pricepoint 602 represents anadvertiser's budget for the advertising campaign. The budget can beallocated across multiple slots, and across multiple media conduits,according to the inventory and goals for the campaign. Goals may includethe target audience desired to be reached, and the hoped for number ofimpressions.

A consumer graph is, or has been, constructed 610, or is continuallyunder construction and revision, according to methods describedelsewhere herein, and a pool of consumers is defined 612, based on thegraph of consumer properties, wherein the graph contains informationabout the devices used by each consumer and demographic data on eachconsumer, and wherein the pool of consumers contains consumers having atleast a threshold similarity to a member of a target audience. Thesystem receives a list of advertising inventory 620 from one or moremedia conduits or content providers, wherein the list of inventorycomprises one or more slots for TV and online. Based on the pool ofconsumers, the campaign descriptions and available inventory, the systemidentifies one or more advertising targets 630, wherein each of the oneor more advertising targets comprises a sequence of slots, consistentwith a given pricepoint 602 associated with a campaign description 600.It is then possible to allocate the advertising content of the one ormore campaign descriptions to the one or more advertising targets 640based on the inventory.

The foregoing steps may be carried out in orders other than as describedabove, or iteratively, sequentially, or simultaneously, in part. Thus,the system may receive the campaign descriptions and pricepoint(s), atthe same time as it receives advertising inventory, or beforehand, orafterwards. The consumer graph, additionally, may be continually beingupdated.

Inputs to the system of the various categories of data (inventory,advertising campaigns, etc.) can be via various application programinterfaces (APIs), the development of which is within the capability ofone skilled in the art.

Then, for each slot in the sequence of slots, the system makes a bid onthe slot consistent with the pricepoint; for a first slot where a bid isa winning bid, the system then instructs a first content provider todeliver a first item of advertising content in the first slot and afirst performance tag to the pool of consumers on a first device. Thesystem can receive a first datum from the first tag for the first itemof advertising content to validate whether a particular consumer viewedthe first item of advertising content on the first device; and dependingon the first datum, for a second slot where a bid is a winning bid, canthen instruct a second content provider to deliver a second item ofadvertising content in the second slot and a second tag to theparticular consumer on a second device. It is preferable that at leastone of the first device and the second device is a TV. A given datum ofcontent can be a beacon, such as communicated via a protocol such asVPAID or VAST.

The system can further obtain a second datum for the second item ofadvertising content, and, optionally, deliver an additional item ofadvertising content to an additional slot of inventory accessible to theconsumer, based on the second datum. In some embodiments, the additionalitem of advertising content is referred to as a “TV extension” if it isdelivered to a consumer online, such as on the consumer's mobile deviceor desktop computer after the consumer has seen a prior portion of theadvertising content on a TV.

In one embodiment, the first item of advertising content and the seconditem of advertising content are sequential parts of a narrative.

In some embodiments, the first performance indicator can comprise aconfirmation of whether the consumer has seen the first item ofadvertising content, in which case the second item of advertisingcontent is not delivered to the consumer until the consumer has seen thefirst item of advertising content.

In some embodiments, the pool of consumers comprises a first group ofconsumers having a first threshold similarity to a member of the targetaudience, and a second group of consumers having a second thresholdsimilarity to a member of the target audience, and wherein the firstitem of advertising content is present in two versions, and the firstversion is delivered to a first slot of inventory accessible to thefirst group of consumers and a second version is delivered to a secondslot of inventory accessible to the second group of consumers.

In some embodiments, the first performance indicator comprises anindication of whether the consumer has skipped or declined to view thefirst item of advertising content, and the second item of advertisingcontent is not delivered to the consumer if the consumer has skipped ordeclined to view the first item of advertising content.

In some embodiments, the first performance indicator comprises anindication of whether the consumer has purchased a product featured inthe first item of advertising content, and the second item ofadvertising content is not delivered to the consumer if the consumer haspurchased the product.

In various embodiments, the first slot and second slot are both on amobile device, or the first and second slots are on different devices,such as respectively a TV and a mobile device, or a mobile device and aTV.

In some embodiments, the second performance indicator for the seconditem of advertising content includes a gross rating point, and if thegross rating point is below a target number, one or more of the firstand second items of advertising content, or a third item of advertisingcontent, is delivered to a third slot of inventory accessible to theconsumer on a TV.

The advertiser is provided with an intuitive front-end interface throughwhich they can upload their campaign content (videos and short clips),and select a type of campaign implementation. This can include, forexample, selecting sequential messaging, selecting a targeted segment ofthe market, selecting preferred content providers, and the like.

The system therefore permits flexibility in targeting of specific marketsegments and provides controls over targeting and scheduling with highprecision.

Implementation of Campaign Variations

While the technology can provide for sequential delivery of content,other campaign models can also be implemented. Thus, overall, thetechnology provides for: story-based campaigns, version basedapproaches, suppression-based approaches, and conquest marketing.

A story-based campaign is implemented in a linear order of two or morechapters that are part of a single campaign. The campaign is deliveredin relative order to a single consumer. There is survey data to suggestthat this type of campaign is more effective in engaging consumerresponse than single adverts delivered repeatedly. In fact, some studiesshow that there is a negative impact on a brand when advertising becomesover redundant.

A version-based approach to advertising involves identifying andmatching the most effective version of an advertisement to a givenportion of the overall audience based on their classification.Classifications are established by data analysis of a combination ofconsumer behavior and census data. A version-based approach can beeffective because people have different tastes and preferences, whichcan be assessed. An advertiser may then choose to invest in advertisingcontent that elicits deeper and more engaging responses from certainsubsets of the market. The methods herein can include performingcorrections or normalizations of other consumer data based on censusdata, if, for example, there is a mismatch for the demographic data fromcensus and that from other sources. This might occur if the majority ofconsumer data from an area reflects incomes of relatively high income,yet the area as a whole is impoverished, with only pockets of wealth.

A suppression-based approach focuses on the customer response toadvertising content that has been viewed. For example, an advertiser maychoose not to air additional impressions of an advertisement to a givenconsumer if that consumer has consistently chosen to skip priorimpressions of the advertisement or frequently skips certain types ofadvertisement, for example, by clicking the “Skip” option on a video.Alternatively, if consumer data reports that a consumer has purchasedthe product in question, the system can automatically move that consumerto a “suppression segment” list that will not receive subsequentinstances of the advertisement. By doing so, advertisers can potentiallygain a better return-on-investment by not over-advertising to customersthat have already purchased the product they are trying to sell.

Conquest marketing (“competitive conquesting”) involves an advertiserdetecting whether a consumer has been exposed to a competitor'sadvertisement, and using that fact to trigger the advertiser's owncampaign; could try to defeat this through TV extension): If theuser-advertiser finds that a competitor is heavily advertising on aparticular media conduit, such as TV, they can make the strategicdecision to implement a campaign targeting similar audiences onalternative media conduits such as OTT or mobile. The user-advertiser isable to gain more effective content leveraging by alternatingadvertising placement. If the user-advertiser has selected sequentialmessaging, it is still able to change media conduits at any time withinthe linear story such that if chapters 1-3 are aired on TV, chapters 2-5can be moved to OTT or mobile. In this implementation, advertisers nowhave flexibility and transparency to be strategic in responding tocompetitors who buy competing impressions rather than feeling tied toexisting commitments.

In preferred embodiments, sequential analytics can be provided. Forexample, it may be desirable to compare the efficacy of the sequences TVto mobile, or mobile to TV.

Optimization

A sequentially targeted advertisement campaign can also be optimized by,for example, applying a machine learning technique to the first andsecond performance tags in a campaign when run initially, or afterhaving been run several times, or in conjunction with results fromrunning one or more other campaigns. A goal of such an optimization maybe to improve allocating the advertising content of the one or morecampaign descriptions to the one or more advertising targets. The costeffectiveness of a campaign can also be used as the target ofoptimization: for example, the cost per consumer reached (knowing thetotal cost of advertisement placement) can be calculated and optimizedin order to continually refine the campaign.

Computational Implementation

The computer functions for manipulations of advertising campaign data,advertising inventory, and consumer and device graphs, inrepresentations such as bit-strings, can be developed by a programmer ora team of programmers skilled in the art. The functions can beimplemented in a number and variety of programming languages, including,in some cases mixed implementations. For example, the functions as wellas scripting functions can be programmed in functional programminglanguages such as: Scala, Golang, and R. Other programming languages maybe used for portions of the implementation, such as Prolog, Pascal, C,C++, Java, Python, VisualBasic, Perl, .Net languages such as C#, andother equivalent languages not listed herein. The capability of thetechnology is not limited by or dependent on the underlying programminglanguage used for implementation or control of access to the basicfunctions. Alternatively, the functionality could be implemented fromhigher level functions such as tool-kits that rely on previouslydeveloped functions for manipulating mathematical expressions such asbit-strings and sparse vectors.

The technology herein can be developed to run with any of the well-knowncomputer operating systems in use today, as well as others, not listedherein. Those operating systems include, but are not limited to: Windows(including variants such as Windows XP, Windows95, Windows2000, WindowsVista, Windows 7, and Windows 8, Windows Mobile, and Windows 10, andintermediate updates thereof, available from Microsoft Corporation);Apple iOS (including variants such as iOS3, iOS4, and iOS5, iOS6, iOS7,iOS8, and iOS9, and intervening updates to the same); Apple Macoperating systems such as OS9, OS 10.x (including variants known as“Leopard”, “Snow Leopard”, “Mountain Lion”, and “Lion”; the UNIXoperating system (e.g., Berkeley Standard version); and the Linuxoperating system (e.g., available from numerous distributors of free or“open source” software).

To the extent that a given implementation relies on other softwarecomponents, already implemented, such as functions for manipulatingsparse vectors, and functions for calculating similarity metrics ofvectors, those functions can be assumed to be accessible to a programmerof skill in the art.

Furthermore, it is to be understood that the executable instructionsthat cause a suitably-programmed computer to execute the methodsdescribed herein, can be stored and delivered in any suitablecomputer-readable format. This can include, but is not limited to, aportable readable drive, such as a large capacity “hard-drive”, or a“pen-drive”, such as connects to a computer's USB port, an internaldrive to a computer, and a CD-Rom or an optical disk. It is further tobe understood that while the executable instructions can be stored on aportable computer-readable medium and delivered in such tangible form toa purchaser or user, the executable instructions can also be downloadedfrom a remote location to the user's computer, such as via an Internetconnection which itself may rely in part on a wireless technology suchas WiFi. Such an aspect of the technology does not imply that theexecutable instructions take the form of a signal or other non-tangibleembodiment. The executable instructions may also be executed as part ofa “virtual machine” implementation.

The technology herein is not limited to a particular web browser versionor type; it can be envisaged that the technology can be practiced withone or more of: Safari, Internet Explorer, Edge, FireFox, Chrome, orOpera, and any version thereof.

Computing apparatus

An exemplary general-purpose computing apparatus 900 suitable forpracticing the methods described herein is depicted schematically inFIG. 7.

The computer system 900 comprises at least one data processing unit(CPU) 922, a memory 938, which will typically include both high speedrandom access memory as well as non-volatile memory (such as one or moremagnetic disk drives), a user interface 924, one more disks 934, and atleast one network or other communication interface connection 936 forcommunicating with other computers over a network, including theInternet, as well as other devices, such as via a high speed networkingcable, or a wireless connection. There may optionally be a firewall 952between the computer and the Internet. At least the CPU 922, memory 938,user interface 924, disk 934 and network interface 936, communicate withone another via at least one communication bus 933.

CPU 922 may optionally include a vector processor, optimized formanipulating large vectors of data.

Memory 938 stores procedures and data, typically including some or allof: an operating system 940 for providing basic system services; one ormore application programs, such as a parser routine 950, and a compiler(not shown in FIG. 7), a file system 942, one or more databases 944 thatstore advertising inventory 946, campaign descriptions 948, and otherinformation, and optionally a floating point coprocessor where necessaryfor carrying out high level mathematical operations. The methods of thepresent invention may also draw upon functions contained in one or moredynamically linked libraries, not shown in FIG. 9, but stored either inmemory 938, or on disk 934.

The database and other routines shown in FIG. 7 as stored in memory 938may instead, optionally, be stored on disk 934 where the amount of datain the database is too great to be efficiently stored in memory 938. Thedatabase may also instead, or in part, be stored on one or more remotecomputers that communicate with computer system 900 through networkinterface 936.

Memory 938 is encoded with instructions for receiving input from one ormore advertisers and for calculating a similarity score for consumersagainst one another. Instructions further include programmedinstructions for performing one or more of parsing, calculating ametric, and various statistical analyses. In some embodiments, thesparse vector themselves are not calculated on the computer 900 but areperformed on a different computer and, e.g., transferred via networkinterface 936 to computer 900.

Various implementations of the technology herein can be contemplated,particularly as performed on computing apparatuses of varyingcomplexity, including, without limitation, workstations, PC's, laptops,notebooks, tablets, netbooks, and other mobile computing devices,including cell-phones, mobile phones, wearable devices, and personaldigital assistants. The computing devices can have suitably configuredprocessors, including, without limitation, graphics processors, vectorprocessors, and math coprocessors, for running software that carries outthe methods herein. In addition, certain computing functions aretypically distributed across more than one computer so that, forexample, one computer accepts input and instructions, and a second oradditional computers receive the instructions via a network connectionand carry out the processing at a remote location, and optionallycommunicate results or output back to the first computer.

Control of the computing apparatuses can be via a user interface 924,which may comprise a display, mouse 926, keyboard 930, and/or otheritems not shown in FIG. 7, such as a track-pad, track-ball,touch-screen, stylus, speech-recognition, gesture-recognitiontechnology, or other input such as based on a user's eye-movement, orany subcombination or combination of inputs thereof. Additionally,implementations are configured that permit a purchaser of advertisinginventory to access computer 900 remotely, over a network connection,and to view inventory via an interface having attributes comparable tointerface 924.

In one embodiment, the computing apparatus can be configured to restrictuser access, such as by scanning a QR-code, gesture recognition,biometric data input, or password input.

The manner of operation of the technology, when reduced to an embodimentas one or more software modules, functions, or subroutines, can be in abatch-mode—as on a stored database of inventory and consumer data,processed in batches, or by interaction with a user who inputs specificinstructions for a single advertising campaign.

The results of matching advertising inventory to criteria for anadvertising campaign, as created by the technology herein, can bedisplayed in tangible form, such as on one or more computer displays,such as a monitor, laptop display, or the screen of a tablet, notebook,netbook, or cellular phone. The results can further be printed to paperform, stored as electronic files in a format for saving on acomputer-readable medium or for transferring or sharing betweencomputers, or projected onto a screen of an auditorium such as during apresentation.

ToolKit: The technology herein can be implemented in a manner that givesa user (such as a purchaser of advertising inventory) access to, andcontrol over, basic functions that provide key elements of advertisingcampaign management. Certain default settings can be built in to acomputer-implementation, but the user can be given as much choice aspossible over the features that are used in assigning inventory, therebypermitting a user to remove certain features from consideration oradjust their weightings, as applicable.

The toolkit can be operated via scripting tools, as well as or insteadof a graphical user interface that offers touch-screen selection, and/ormenu pull-downs, as applicable to the sophistication of the user. Themanner of access to the underlying tools by a user is not in any way alimitation on the technology's novelty, inventiveness, or utility.

Accordingly, the methods herein may be implemented on or across one ormore computing apparatuses having processors configured to execute themethods, and encoded as executable instrucitons in computer readablemedia.

For example, the technology herein includes computer readable mediaencoded with instructions for executing a method for deliveringadvertising content sequentially to a consumer across two or moredisplay devices, the instructions including but not limited to:instructions for receiving a pricepoint and one or more campaigndescriptions from an advertiser; instructions for defining a pool ofconsumers based on a graph of consumer properties, wherein the graphcontains information about two or more TV and mobile devices used byeach consumer, demographic and online behavioral data on each consumerand similarities between pairs of consumers, and wherein the pool ofconsumers comprises consumers having at least a threshold similarity toa member of the target audience; instructions for receiving a list ofinventory from one or more content providers, wherein the list ofinventory comprises one or more slots for TV and online; instructionsfor identifying one or more advertising targets, wherein each of the oneor more advertising targets comprises a sequence of slots consistentwith one or more of the campaign descriptions, and an overall costconsistent with the pricepoint; instructions for allocating theadvertising content of the one or more campaign descriptions to the oneor more advertising targets; instructions for making bids on slots ofadvertising inventory; and instructions for communicating with one ormore content providers to deliver items of advertising content and forreceiving performance data from the providers.

The technology herein may further comprise computer-readable mediaencoded with instructions for executing a method for deliveringadvertising content sequentially to a consumer across two or moredisplay devices, the instructions including: instructions foridentifying a consumer based on one or more demographic factors, andbased on one or more indices of similarity to members of a targetaudience; instructions for identifying two or more display devicesaccessible to the consumer, wherein the two or more display devicescomprise at least one TV and at least one mobile device; instructionsfor purchasing two or more slots of advertising inventory wherein one ormore slots are delivered on a TV, and one or more slots are delivered ona mobile device; instructions for communicating with one or more mediaconduits to deliver a items of advertising content to consumers onvarious display devices, and receiving processing confirmations from theproviders, wherein at least one of the devices is a TV, and at least oneof the devices is a mobile device.

The technology herein may further comprise computer-readable mediaencoded with instructions for executing a method of controllingsequential delivery of cross-screen advertising content to a consumer,the instructions including instructions for: determining that theconsumer is a member of a target audience; identifying a first andsecond device accessible to the consumer; receiving input for placementof a first and second item of advertising content on the first andsecond device, consistent with an advertising budget and the targetaudience; causing a first media conduit to deliver the first item ofadvertising content to the first device; and when the first item ofadvertising content has been viewed by the consumer, causing a secondmedia conduit to deliver the second item of advertising content to thesecond device, wherein the first and second device comprise a TV and amobile device.

Correspondingly, the technology herein also includes computing apparatushaving at least one processor configured to execute instructions forimplementing a method for delivering advertising content sequentially toa consumer across two or more display devices, the instructionsincluding but not limited to: instructions for receiving a pricepointand one or more campaign descriptions from an advertiser; instructionsfor defining a pool of consumers based on a graph of consumerproperties, wherein the graph contains information about two or more TVand mobile devices used by each consumer, demographic and onlinebehavioral data on each consumer and similarities between pairs ofconsumers, and wherein the pool of consumers comprises consumers havingat least a threshold similarity to a member of the target audience;instructions for receiving a list of inventory from one or more contentproviders, wherein the list of inventory comprises one or more slots forTV and online; instructions for identifying one or more advertisingtargets, wherein each of the one or more advertising targets comprises asequence of slots consistent with one or more of the campaigndescriptions, and an overall cost consistent with the pricepoint;instructions for allocating the advertising content of the one or morecampaign descriptions to the one or more advertising targets;instructions for making bids on slots of advertising inventory; andinstructions for communicating with one or more content providers todeliver items of advertising content and for receiving performance datafrom the providers.

Furthermore, the technology herein may further include a computingapparatus having at least one processor configured to executeinstructions for implementing a method for delivering advertisingcontent sequentially to a consumer across two or more display devices,the instructions including: instructions for identifying a consumerbased on one or more demographic factors, and based on one or moreindices of similarity to members of a target audience; instructions foridentifying two or more display devices accessible to the consumer,wherein the two or more display devices comprise at least one TV and atleast one mobile device; instructions for purchasing two or more slotsof advertising inventory wherein one or more slots are delivered on aTV, and one or more slots are delivered on a mobile device; instructionsfor communicating with one or more media conduits to deliver a items ofadvertising content to consumers on various display devices, andreceiving processing confirmations from the providers, wherein at leastone of the devices is a TV, and at least one of the devices is a mobiledevice.

The technology herein may further comprise computer-readable mediaencoded with instructions for executing a method of controllingsequential delivery of cross-screen advertising content to a consumer,the instructions including instructions for: determining that theconsumer is a member of a target audience; identifying a first andsecond device accessible to the consumer; receiving input for placementof a first and second item of advertising content on the first andsecond device, consistent with an advertising budget and the targetaudience; causing a first media conduit to deliver the first item ofadvertising content to the first device; and when the first item ofadvertising content has been viewed by the consumer, causing a secondmedia conduit to deliver the second item of advertising content to thesecond device, wherein the first and second device comprise a TV and amobile device.

Cloud Computing

The methods herein can be implemented to run in the “cloud.” Thus theprocesses that one or more computer processors execute to carry out thecomputer-based methods herein do not need to be carried out by a singlecomputing machine or apparatus. Processes and calculations can bedistributed amongst multiple processors in one or more datacenters thatare physically situated in different locations from one another. Data isexchanged with the various processors using network connections such asthe Internet. Preferably, security protocols such as encryption areutilized to minimize the possibility that consumer data can becompromised. Calculations that are performed across one or morelocations remote from an entity such as a DSP include calculation ofconsumer and device graph, and updates to the same.

EXAMPLES Example 1 Implementation

FIGS. 8-1 and 8-2 show an exemplary embodiment of an implementation thatallows advertisers to choose inventory according to a bid price. Aninterface for placing ads within programmatic TV content shows a list ofTV schedules in particular geographic regions and key data such as thenumber of estimated impressions. Other similar interfaces can beenvisaged to allow an advertiser to place content in apps, web, and VODenvironments.

All references cited herein are incorporated by reference in theirentireties.

The foregoing description is intended to illustrate various aspects ofthe instant technology. It is not intended that the examples presentedherein limit the scope of the appended claims. The invention now beingfully described, it will be apparent to one of ordinary skill in the artthat many changes and modifications can be made thereto withoutdeparting from the spirit or scope of the appended claims.

What is claimed:
 1. A method for delivering advertising contentsequentially to a consumer across two or more display devices, themethod comprising: receiving a pricepoint and one or more campaigndescriptions from an advertiser, wherein each of the campaigndescriptions comprises a schedule for sequential delivery of two or moreitems of advertising content across two or more devices accessed by aconsumer, wherein the devices include a TV and one or more mobiledevices, and a target audience, wherein the target audience is definedby one or more demographic factors; defining a pool of consumers basedon a graph of consumer properties, wherein the graph containsinformation about the two or more TV and mobile devices used by eachconsumer, demographic and online behavioral data on each consumer andsimilarities between pairs of consumers, and wherein the pool ofconsumers comprises consumers having at least a threshold similarity toa member of the target audience; receiving a list of inventory from oneor more content providers, wherein the list of inventory comprises oneor more slots for TV and online; identifying one or more advertisingtargets, wherein each of the one or more advertising targets comprises asequence of slots consistent with one or more of the campaigndescriptions, and an overall cost consistent with the pricepoint;allocating the advertising content of the one or more campaigndescriptions to the one or more advertising targets; for each slot inthe sequence of slots, making a bid on the slot consistent with thepricepoint; for a first slot where a bid is a winning bid: instructing afirst content provider to deliver a first item of advertising content inthe first slot and a first performance tag to the pool of consumers on afirst device; receiving a first datum from the first performance tag tovalidate whether a particular consumer viewed the first item ofadvertising content on the first device; and depending on the firstdatum, for a second slot where a bid is a winning bid, instructing asecond content provider to deliver a second item of advertising contentin the second slot and a second performance tag to the particularconsumer on a second device, wherein at least one of the first deviceand the second device is a TV.
 2. The method of claim 1, furthercomprising: obtaining a second datum from the second performance tag forthe second item of advertising content; and, optionally, instructing athird content provider to deliver an additional item of advertisingcontent to an additional slot accessible to the consumer on a thirddevice, based on the second datum, wherein the third device isoptionally the same as either the first device or the second device. 3.The method of claim 1, wherein the first item of advertising content andthe second item of advertising content are sequential parts of anarrative.
 4. The method of claim 1, wherein the first datum comprises aconfirmation whether the consumer has seen the first item of advertisingcontent, and the second item of advertising content is not delivered tothe consumer until the consumer has seen the first item of advertisingcontent.
 5. The method of claim 1, wherein the pool of consumerscomprises a first group of consumers having a first threshold similarityto a member of the target audience, and a second group of consumershaving a second threshold similarity to a member of the target audience,and wherein the first item of advertising content is present in twoversions, and the first version is delivered to a first slot ofinventory accessible to the first group of consumers and a secondversion is delivered to a second slot of inventory accessible to thesecond group of consumers.
 6. The method of claim 1, wherein the firstdatum comprises an indication of whether the consumer has skipped ordeclined to view the first item of advertising content, and the seconditem of advertising content is not delivered to the consumer if theconsumer has skipped or declined to view the first item of advertisingcontent.
 7. The method of claim 1, wherein the first datum comprises anindication of whether the consumer has purchased a product featured inthe first item of advertising content, and the second item ofadvertising content is not delivered to the consumer if the consumer haspurchased the product.
 8. The method of claim 1, wherein the first slotis on a TV, and the second slot is on a mobile device.
 9. The method ofclaim 8, wherein a second performance tag for the second item ofadvertising content includes a gross rating point, and wherein if thegross rating point is below a target number, one or more of the firstand second items of advertising content, or a third item of advertisingcontent, is delivered to a third slot of inventory accessible to theconsumer on a TV.
 10. A method for optimizing an advertising campaign,the method comprising: receiving a pricepoint and one or more campaigndescriptions from an advertiser, wherein each of the campaigndescriptions comprises a schedule for sequential delivery of one or moreitems of advertising content across two or more devices accessed by aconsumer, wherein the devices include a TV and one or more mobiledevices, and a target audience, wherein the target audience is definedby one or more demographic factors selected from: age range, gender, andlocation; defining a pool of consumers based on a graph of consumerproperties, wherein the graph contains information about the devicesused by each consumer, demographic data on each consumer andsimilarities between pairs of consumers, and wherein the pool ofconsumers comprises consumers having at least a threshold similarity toa member of the target audience; receiving a list of inventory from oneor more content providers, wherein the list of inventory comprises oneor more segments for TV and online; identifying one or more advertisingtargets, wherein each of the one or more advertising targets comprises asequence of slots consistent with one or more of the campaigndescriptions, and an overall cost consistent with the pricepoint;allocating the advertising content of the one or more campaigndescriptions to the one or more advertising targets based on theinventory; for each slot in the sequence of slots, making a bid on theslot consistent with the pricepoint; for a first slot where a bid is awinning bid: instructing a first content provider to deliver a firstitem of advertising content in the first slot and a first performancetag to the pool of consumers on a first device; receiving a first datumfrom the first performance tag to validate whether a particular consumerviewed the first item of advertising content on the first device; anddepending on the first datum, for a second slot where a bid is a winningbid, instructing a second content provider to deliver a second item ofadvertising content in the second slot and a second performance tag tothe particular consumer on a second device, wherein at least one of thefirst device and the second device is a TV; receiving a second datumfrom the second performance tag to validate whether a particularconsumer viewed the second item of advertising content on the seconddevice; and applying a machine learning technique to the first andsecond performance tags, in order to improve the allocating theadvertising content of the one or more campaign descriptions to the oneor more advertising targets.
 11. A method of controlling sequentialdelivery of cross-screen advertising content to a consumer, the methodcomprising: determining that the consumer is a member of a targetaudience; identifying a first and second device accessible to theconsumer; receiving instructions for placement of a first and seconditem of advertising content on the first and second device, consistentwith an advertising budget and the target audience; causing a firstmedia conduit to deliver the first item of advertising content to thefirst device; and when the first item of advertising content has beenviewed by the consumer, causing a second media conduit to deliver thesecond item of advertising content to the second device, wherein thefirst and second device comprise a TV and a mobile device.